The statements in this section merely provide background information related to the disclosure and may not constitute prior art.
The annotations (i.e., labels) of the vertebrae and inter-vertebral discs in axial spine Magnetic Resonance Image (MRI) images (i.e., slices) are essential for further diagnosis of various spine disorders. For instance, in MRI, annotating the axial-view slices facilitates the quantification and level-based reporting of common inter-vertebral disc displacements such as protrusion, extrusion, and bulging.
Generating labels in a manual fashion is tedious, subjective, and time-consuming especially because the number of 2D slices in a series can be very high, with up to 100 images per axial series. Furthermore, it can be difficult for radiologists to determine the identity of a particular vertebra in an axial slice of the spine, because adjacent vertebrae have very similar appearances. Even when a particular vertebra has been identified in a particular axial slice, a radiologist must remember its identity and location as they make a diagnosis from multiple axial slices in a series.
Current spine labeling algorithms focus on the sagittal view only. However, the quantification and level-based reporting of common inter-vertebral disc displacements such as protrusion, extrusion, and bulging require the radiologist to thoroughly inspect all individual axial slices while visually cross-referencing such axial slices to their corresponding position in the sagittal view. This requires labeling the sagittal view, which has at least two limitations. First, sagittal images are not always available for every patient (i.e., only the axial view may be available) while in other cases the two scans might be acquired at different time points. In such cases, when deformations between sagittal and axial images occur because of patient repositioning, cross-referencing may not be reliable. Therefore, localizing the spinal structures in different views becomes challenging (even for an experienced radiologist). Second, using a sagittal-view labeling along with the cross-reference feature allows to label not all, but only a few slices in the axial series.
Most of the current spine labeling algorithms address the above described labeling limitations through intensive external training from a manually-labeled data set. Such training aims to refine the algorithms to learn the shapes, textures and appearances of different spinal structures. This knowledge is then used within a classification or regression algorithm such as support vector machine, random forest regression or graphical models to subsequently label different spinal structures in the test image. Such algorithms work very well on data sets that closely match the training data, but require adjustment/retraining for different data sets or if the imaging modality and/or acquisitions protocol are altered (e.g., an algorithm that is trained and built for Computerized Tomography (CT) images may not perform well on Magnetic Resonance (MR) data). This impedes the use of these algorithms in routine clinical practices, where a particular disorder might be analyzed radiologically using several different imaging modalities/protocols with widely variable imaging parameters.